Biblio

With the rapid development of the information technology era, the era of big data has also arrived. While computer networks are promoting the prosperity and development of society, their applications have become more extensive and in-depth. Smart city video surveillance systems have entered an era of networked surveillance and business integration. The problems are also endless. This article discusses computer network security in the era of big data, hoping to help strengthen the security of computer networks in our country. This paper studies the computer network security prevention strategies of smart cities in the era of big data.

The growth of inter-dependency intricacies of Supervisory Control and Data Acquisition (SCADA) systems in industrial operations generates a likelihood of increased vulnerability to malicious threats and machine learning approaches have been extensively utilized in the research for vulnerability detection. Nonetheless, to improve security, an enhanced vulnerability detection using hyper-parameter-tune machine learning is proposed for early detection, classification and mitigation of SCADA communication and transmission networks by classifying benign, or malicious DNS attacks. The proposed scheme, an ensemble optimizer (GentleBoost) upon hyper-parameter tuning, gave a comparative achievement. From the simulation results, the proposed scheme had an outstanding performance within the shortest possible time with an accuracy of 99.49%, 99.23% for precision, and a recall rate of 99.75%. Also, the model was compared to other contemporary algorithms and outperformed all the other algorithms proving to be an approach to keep abreast of the SCADA network vulnerabilities and attacks.

To address the concerns posed by certain security attacks on communication protocol, this paper proposes a Quantum Key Exchange algorithm coupled with an encoding scheme based on Fermat Numbers and DNA sequences. The concept of Watson-Crick’s transformation of DNA sequences and random property of the Fermat Numbers is applied for protection of the communication system by means of dual encryption. The key generation procedure is governed by a quantum bit rotation mechanism. The total process is illustrated with an example. Also, security analysis of the encryption and decryption process is also discussed.

This paper aims to present a performance comparison of new authenticated encryption (AE) algorithm with the objective of high network security and better efficiency as compared to the defacto standard. This algorithm is based on a critical property of nonce-misuse-resistance incorporating DNA computation for securing the key, here the processing unit of DNA block converts the input key into its equivalent DNA base formats based on the ASCII code table. The need for secure exchange of keys through a public channel has become inevitable and thus, the proposed architecture will enhance the secrecy by using DNA cryptography. These implementations consider Advanced Encryption Standard in Galois Counter mode (AES-GCM) as a standard for comparison.

With the progress of science and technology and the development of Internet technology, Internet technology has penetrated into various industries in today’s society. But this explosive growth is also troubling information security. Among them, XSS (cross-site scripting vulnerability) is one of the most influential vulnerabilities in Internet applications in recent years. Traditional network security detection technology is becoming more and more weak in the new network environment, and deep learning methods such as CNN and RNN can only learn the spatial or timing characteristics of data samples in a single way. In this paper, a generalized self-regression pretraining model XLNet and GRU XSS attack detection method is proposed, the self-regression pretrained model XLNet is introduced and combined with GRU to learn the time series and spatial characteristics of the data, and the generalization capability of the model is improved by using dropout. Faced with the increasingly complex and ever-changing XSS payload, this paper refers to the character-level convolution to establish a dictionary to encode the data samples, thus preserving the characteristics of the original data and improving the overall efficiency, and then transforming it into a two-dimensional spatial matrix to meet XLNet’s input requirements. The experimental results on the Github data set show that the accuracy of this method is 99.92 percent, the false positive rate is 0.02 percent, the accuracy rate is 11.09 percent higher than that of the DNN method, the false positive rate is 3.95 percent lower, and other evaluation indicators are better than GRU, CNN and other comparative methods, which can improve the detection accuracy and system stability of the whole detection system. This multi-model fusion method can make full use of the advantages of each model to improve the accuracy of system detection, on the other hand, it can also enhance the stability of the system.

In view of the current status of Internet of Things applications and related security problems, the architecture system of Internet of Things applications based on block chain is introduced. First, it introduces the concepts related to blockchain technology, introduces the architecture system of iot application based on blockchain, and discusses its overall architecture design, key technologies and functional structure design. The product embodies the whole process of the Internet of Things platform on the basis of blockchain, which builds an infrastructure based on the Internet of Things and solves the increasingly serious security problems in the Internet of Things through the technical characteristics of decentralization.

With the development of cloud computing technology, cloud services have been used by more and more traditional applications and products because of their unique advantages such as virtualization, high scalability and universality. In the cloud computing environment, computer networks often encounter security problems such as external attacks, hidden dangers in the network and hidden dangers in information sharing. The network security level protection system is the basic system of national network security work, which is the fundamental guarantee for promoting the healthy development of informatization and safeguarding national security, social order and public interests. This paper studies cloud computing security from the perspective of level protection, combining with the characteristics of cloud computing security. This scheme is not only an extension of information system level protection, but also a study of cloud computing security, aiming at cloud computing security control from the perspective of level protection.

Renewed focus on spacecraft networking by government and private industry promises to establish interoperable communications infrastructures and enable distributed computing in multi-nodal systems. Planned near-Earth and cislunar missions by NASA and others evidence the start of building this networking vision. Working with space agencies, academia, and industry, NASA has developed a suite of communications protocols and algorithms collectively referred to as Delay-Tolerant Networking (DTN) to support an interoperable space network. Included in the DTN protocol suite is a security protocol - the Bundle Protocol Security Protocol - which provides the kind of delay-tolerant, transport-layer security needed for cislunar and deep-space trusted networking. We present an analysis of the lifecycle of security operations inherent in a space network with a focus on the DTN-enabled space networking paradigm. This analysis defines three security-related roles for spacecraft (Security Sources, verifiers, and acceptors) and associates a series of critical processing events with each of these roles. We then define the set of required and optional actions associated with these security events. Finally, we present a series of best practices associated with policy configurations that are unique to the space-network security problem. Framing space network security policy as a mapping of security actions to security events provides the details necessary for making trusted networks semantically interoperable. Finally, this method is flexible enough to allow for customization even while providing a unifying core set of mandatory security actions.

Firewall is the first defense line for network security. Packet filtering is a basic function in firewall, which filter network packets according to a series of rules called firewall policy. The design of firewall policy is invariably under the instruction of security policy, which is a generic guideline that lists the needs for network access permissions. The design of firewall policy should observe the regulations of security policy. However, even for IPv4 firewall policy, it is extremely difficult to keep the consistency between security policy and firewall policy. Some consistency decision methods of security policy and IPv4 firewall policy were proposed. However, the address space of IPv6 address is a very large, the existing consistency decision methods can not be directly used to deal with IPv6 firewall policy. To resolve the above problem, in this paper, we use a formal technique to decide the consistency between IPv6 firewall policy and security policy effectively and rapidly. We also developed a prototype model and evaluated the effectiveness of the proposed method.

The Interface to Network Security Functions (I2NSF) Working Group in Internet Engineering Task Force (IETF) provides data models of interfaces to easily configure Network Security Functions (NSF). The Working Group presents a high-level data model and a low-level data model for configuring the NSFs. The high-level data model is used for the users to manipulate the NSFs configuration easily without any security expertise. But the NSFs cannot be configured using the high-level data model as it needs a low-level data model to properly deploy their security operation. For that reason, the I2NSF Framework needs a security policy translator to translate the high-level data model into the corresponding low-level data model. This paper improves the previously proposed Security Policy Translator by adding an Automatic Data Model Mapper. The proposed mapper focuses on the mapping between the elements in the high-level data model and the elements in low-level data model to automate the translation without the need for a security administrator to create a mapping table.

Cyber attacks are a major concern for network administrators as the occurrences of such events are continuously increasing on the Internet. Software-defined networks (SDN) enable many management applications, but they may also become targets for attackers. Due to the separation of the data plane and the control plane, the controller appears as a new element in SDN networks, allowing centralized control of the network, becoming a strategic target in carrying out an attack. According to reports generated by security labs, the frequency of the distributed denial of service (DDoS) attacks has seen an increase in recent years, characterizing a major threat to the SDN. However, few research papers address the prevention of DDoS attacks on SDN. Therefore, this work presents a Systematic Mapping of Literature, aiming at identifying, classifying, and thus disseminating current research studies that propose techniques and methods for preventing DDoS attacks in SDN. When answering these questions, it was determined that the SDN controller was vulnerable to possible DDoS attacks. No prevention methods were found in the literature for the first phase of the attack (when attackers try to deceive users and infect the host). Therefore, the security of software-defined networks still needs improvement over DDoS attacks, despite the evident risk of an attack targeting the SDN controller.

Connecting anything to the Internet is one of the main objectives of the Internet of Things (IoT). It enabled to access any device from anywhere at any time without any human intervention. There are endless applications of IoT involving controlling home applications to industry. This rapid growth of this technology and innovations of its application results due to improved technology of developing these tiny devices with its back-end software. On the other side, internal resources such as memory, processing power, battery life are the significant constraints of these devices. Introducing lightweight cryptography helped secure data transmission across various devices while protecting these devices from getting attacked for DDoS attack is still a significant concern. This paper primarily focuses on elaborating on DDoS attack and the malware used to initiate a DDoS attack on IoT devices. Further, this paper mainly focuses on providing solutions that would help to prevent DDoS attack from IoT network.

Several previous studies have focused on Distributed Denial of Service (DDoS) attacks, which are a crucial problem in computer network security. In this paper we explore the applicability of a a time series method known as a matrix profile to the anomaly based DDoS attacks detection. The study thus examined how the matrix profile method performed in diverse situations related to DDoS attacks, as well as identifying those features that are most applicable in various scenarios. Based on reported empirical evaluation the matrix profile method is shown to be efficient against most of the considered types of DDoS attacks.

Network traffic detection is closely related to network security, and it is also a hot research topic now. With the development of encryption technology, traffic detection has become more and more difficult, and many crimes have occurred on the dark web, so how to detect dark web traffic is the subject of this study. In this paper, we proposed a dark web traffic(Tor traffic) detection scheme based on deep learning and conducted experiments on public data sets. By analyzing the results of the experiment, our detection precision rate reached 95.47%.

Modern Intrusion Detection Systems are able to identify and check all traffic crossing the network segments that they are only set to monitor. Traditional network infrastructures use static detection mechanisms that check and monitor specific types of malicious traffic. To mitigate this potential waste of resources and improve scalability across an entire network, we propose a methodology which deploys distributed IDS in a Software Defined Network allowing them to be used for specific types of traffic as and when it appears on a network. The core of our work is the creation of an SDN application that takes input from a Snort IDS instances, thus working as a classifier for incoming network traffic with a static ruleset for those classifications. Our application has been tested on a virtualised platform where it performed as planned holding its position for limited use on static and controlled test environments.

Cloud computing is a modern computing mode based on network, which is widely participated by the public, and provides virtualized dynamic computing resources in the form of services. Cloud computing builds an effective communication platform with the help of computer internet, so that users can get the same computing resources even if they are in different areas. With its unique technical characteristics and advantages, cloud computing has been deployed to practical applications more and more, and the consequent security problems of cloud computing have become increasingly prominent. In addition to the original cloud computing environment, this paper proposes to build a secure cloud with cloud technology, deploy security agents in the business cloud, connect the business cloud, security cloud and security agents through SDN (software defined network) technology, and dynamically divide the business cloud into logically isolated business areas through security agents. Therefore, security is separated from the specific implementation technology and deployment scheme of business cloud, and an information security protection scheme under cloud computing environment is proposed according to the characteristics of various factors, so as to enhance the security of network information.

The growing adoption of IoT devices is creating a huge positive impact on human life. However, it is also making the network more vulnerable to security threats. One of the major threats is malicious traffic injection attack, where the hacked IoT devices overwhelm the application servers causing large-scale service disruption. To address such attacks, we propose a Software Defined Networking based predictive alarm manager solution for malicious traffic detection and mitigation at the IoT Gateway. Our experimental results with the proposed solution confirms the detection of malicious flows with nearly 95% precision on average and at its best with around 99% precision.

Industrial Internet is widely used in the production field. As the openness of networks increases, industrial networks facing increasing security risks. Information and communication technologies are now available for most industrial manufacturing. This industry-oriented evolution has driven the emergence of cloud systems, the Internet of Things (IoT), Big Data, and Industry 4.0. However, new technologies are always accompanied by security vulnerabilities, which often expose unpredictable risks. Industrial safety has become one of the most essential and challenging requirements. In this article, we highlight the serious challenges facing Industry 4.0, introduce industrial security issues and present the current awareness of security within the industry. In this paper, we propose solutions for the anomaly detection and defense of the industrial Internet based on the demand characteristics of network security, the main types of intrusions and their vulnerability characteristics. The main work is as follows: This paper first analyzes the basic network security issues, including the network security needs, the security threats and the solutions. Secondly, the security requirements of the industrial Internet are analyzed with the characteristics of industrial sites. Then, the threats and attacks on the network are analyzed, i.e., system-related threats and process-related threats; finally, the current research status is introduced from the perspective of network protection, and the research angle of this paper, i.e., network anomaly detection and network defense, is proposed in conjunction with relevant standards. This paper proposes a software-defined network (SDN)-based industrial Internet security gateway for the security protection of the industrial Internet. Since there are some known types of attacks in the industrial network, in order to fully exploit the effective information, we combine the ExtratreesClassifier to enhance the detection rate of anomaly detection. In order to verify the effectiveness of the algorithm, this paper simulates an industrial network attack, using the acquired training data for testing. The test data are industrial network traffic datasets, and the experimental results show that the algorithm is suitable for anomaly detection in industrial networks.

With the advancement of network technology, users can now easily gain access to and benefit from networks. However, the number of network violations is increasing. The main issue with this violation is that irresponsible individuals are infiltrating the network. Network intrusion can be interpreted in a variety of ways, including cyber criminals forcibly attempting to disrupt network connections, gaining unauthorized access to valuable data, and then stealing, corrupting, or destroying the data. There are already numerous systems in place to detect network intrusion. However, the systems continue to fall short in detecting and counter-attacking network intrusion attacks. This research aims to enhance the detection of Denial of service (DoS) by identifying significant features and identifying abnormal network activities more accurately. To accomplish this goal, the study proposes an Intrusion Analysis System for detecting Denial of service (DoS) network attacks using machine learning. The accuracy rate of the proposed method using random forest was demonstrated in our experimental results. It was discovered that the accuracy rate with each dataset is greater than 98.8 percent when compared to traditional approaches. Furthermore, when features are selected, the detection time is significantly reduced.

With the popularity of cloud computing, cloud storage technology has also been widely used. Among them, data integrity verification is a hot research topic. At present, the realization of public auditing has become the development trend of integrity verification. Most existing public auditing schemes rarely consider some indispensable functions at the same time. Thus, in this paper, we propose a comprehensive public auditing scheme (PDBPA) that can simultaneously realize data block privacy protection, data dynamics, and multi- user batch auditing. Our PDBPA scheme is implemented in bilinear pairing. By adding random masking in the audit phase, with the help of the characteristics of homomorphic verifiable tags (HVTs), it can not only ensure that the TPA performs the audit work correctly, but also prevent it from exploring the user’s sensitive data. In addition, by utilizing the modified index hash table (MIHT), data dynamics can be effectively achieved. Furthermore, we provide a specific process for the TPA to perform batch audits for multiple users. Moreover, we formally prove the security of the scheme; while achieving the audit correctness, it can resist three types of attacks.

As the extensive use of the wireless sensor networks in Advanced Metering Infrastructure (AMI) of Smart Grid, the network security of AMI becomes more important. Thus, an optimization of trust management mechanism of Beta distribution theory is put forward in this article. First of all, a self-adaption method of trust features sampling is proposed, that adjusts acquisition frequency according to fluctuation of trust attribute collected, which makes the consumption of network resource minimum under the precondition of ensuring accuracy of trust value; Then, the collected trust attribute is judged based on the Mahalanobis distance; Finally, calculate the nodes’ trust value by the optimization of the Beta distribution theory. As the simulation shows, the trust management scheme proposed is suited to WSNs in AMI, and able to reflect the trust value of nodes in a variety of circumstances change better.

Network security is of vital importance, and Information Technology admins must always be vigilant. But they often lack the expertise and skills required to harden the network properly, in with the emergence of security threats. The router plays a significant role in maintaining operational security for an organization. When it comes to information security, information security professionals mainly focus on protecting items such as firewalls, virtual private networks, etc. Routers are the foundation of any network's communication method, which means all the network information passes through the routers, making them a desirable target. The proposed automation of the router security hardening solution will immediately improve the security of routers and ensure that they are updated and hardened with minimal human intervention and configuration changes. This is specially focused on small and medium-sized organizations lacking workforce and expertise on network security and will help secure the routers with less time consumption, cost, and increased efficiency. The solution consists of four primary functions, initial configuration, vulnerability fixing, compliance auditing, and rollback. These focus on all aspects of router security in a network, from its configuration when it is initially connected to the network to checking its compliance errors, continuously monitoring the vulnerabilities that need to be fixed, and ensuring that the behavior of the devices is stable and shows no abnormalities when it comes to configuration changes.

With the rapid development of Internet scale and technology, people pay more and more attention to network security. At present, the general method in the field of network security is to use NSS(Network Security Situation) to describe the security situation of the target network. Because NSSA (Network Security Situation Awareness) has not formed a unified optimal solution in architecture design and algorithm design, many ideas have been put forward continuously, and there is still a broad research space. In this paper, the improved LSTM(long short-term memory) neural network is used to analyze and process NSS data, and effectively utilize the attack logic contained in sequence data. Build NSSF (Network Security Situation Forecast) framework based on NAWL-ILSTM. The framework is to directly output the quantified NSS change curve after processing the input original security situation data. Modular design and dual discrimination engine reduce the complexity of implementation and improve the stability. Simulation results show that the prediction model not only improves the convergence speed of the prediction model, but also greatly reduces the prediction error of the model.

Verification code recognition system based on convolutional neural network. In order to strengthen the network security defense work, this paper proposes a novel verification code recognition system based on convolutional neural network. The system combines Internet technology and big data technology, combined with advanced captcha technology, can prevent hackers from brute force cracking behavior to a certain extent. In addition, the system combines convolutional neural network, which makes the verification code combine numbers and letters, which improves the complexity of the verification code and the security of the user account. Based on this, the system uses threshold segmentation method and projection positioning method to construct an 8-layer convolutional neural network model, which enhances the security of the verification code input link. The research results show that the system can enhance the complexity of captcha, improve the recognition rate of captcha, and improve the security of user accounting.

The purpose of the study is to improve the security of the network, and make the state of network security predicted in advance. First, the theory of neural networks is studied, and its shortcomings are analyzed by the standard Elman neural network. Second, the layers of the feedback nodes of the Elman neural network are improved according to the problems that need to be solved. Then, a network security perception system based on GA-Elman (Genetic Algorithm-Elman) neural network is proposed to train the network by global search method. Finally, the perception ability is compared and analyzed through the model. The results show that the model can accurately predict network security based on the experimental charts and corresponding evaluation indexes. The comparative experiments show that the GA-Elman neural network security perception system has a better prediction ability. Therefore, the model proposed can be used to predict the state of network security and provide early warnings for network security administrators.